The study explores the non-linear relationship between earnings management, governance quality, and profitability in the evolving financial system of Oman. It addresses the issues of endogeneity, persistence, and simultaneity of discretionary provisioning and profitability using a two-step system Generalised Method of Moments (System-GMM) method. It uses a panel dataset of 72 bank-year observations from eight banks listed on the Muscat Stock Exchange over the period 2015-2023. Governance quality is used as a composite indicator of board independence, audit committee activity, and regulatory compliance, while earnings management is represented by discretionary loan loss provisions. The study provides empirical evidence for the inverted U-shaped relationship between discretionary provision and profitability, implying that some income smoothing stabilizes profitability while excessive earnings management reduces profitability. Governance quality has a considerable impact on the relationship between earnings management and profitability, implying that monitoring can help to prevent opportunistic earnings management and reporting. This study provides context-specific insights for the Central Bank of Oman and the Capital Market Authority, highlighting the importance of governance mechanisms in constraining opportunistic earnings management. By focusing on Oman's dual banking system during the IFRS 9 transition and post-pandemic period, the study contributes to the literature by demonstrating how institutional and regulatory characteristics in a small, concentrated banking system shape the relationship between earnings management and profitability.
Alzheimer Disease (AD) is a non-progressive neurodegenerative disease which is a significant challenge to the healthcare system in any country. This is because the timely intervention and personal handling of the patient rests on the timely and precise identification of the risk factors. The presented paper presents a new, data-driven framework which is founded on the implementation of Microsoft Power BI to develop an interactive dashboard on which a visual analysis of the risk factors of AD would be conducted. To manage the research, we will apply the systematic method: (1) the process of data acquisition and pre-processing of a full patient dataset (n=2149), (2) the development and construction of the multi-page interactive dashboard consisting of key performance indicators (KPIs) and dynamic filters, and (3) the comparative visual analysis of the data points to identify the patterns associated with the AD diagnosis. The main technical input is a viable, transferable model of converting complex and multi-dimensional health information into an easy to-use decision-support system. As it is evident in our visual analysis, we also have risk profiles that vary especially higher percentage of behavioural problems (60.24) and low diet quality scores in the AD-diagnosed population. As this article demonstrates, data science methods with visualization tools, can result in actionable information to operate with in researchers and clinicians, generate hypotheses, and discuss the AD field in a data-driven manner.
Despite increasing research on technology-assisted corrective feedback (CF) and its role in L2 development, its impact on formulaic language has remained underexplored. This study examines the effects of two types of ChatGPT-generated CF, staged and direct, on learning three types of lexical bundles, namely those with topical, interpersonal, and textual functions, in EFL learners’ writing. Fifty-eight participants were randomly assigned to one of two experimental groups (staged CF, direct CF) or a control group and completed three treatment sessions. In each session, learners first worked on a gap-filling task with 32 lexical bundles. Those in the staged CF condition asked ChatGPT to identify and underline errors in their drafts before revising and resubmitting them for further correction, whereas those in the direct CF condition received immediate corrections from ChatGPT. Learners’ knowledge and use of lexical bundles were assessed at three time points (pre-test, post-test, and delayed post-test) through a gap-filling task and a free writing task. Results indicated that both feedback conditions significantly outperformed the control group in terms of learning and use of lexical bundles. However, no significant differences emerged between the staged and direct CF groups at either the post-test or delayed post-test. In addition, the findings suggested that both CF types were more effective in promoting learners’ use of interpersonal lexical bundles, whereas they were less effective for textual bundles.
The research work addresses the challenges investors face in tracking and analyzing financial markets, stocks, and cryptocurrencies by developing an advanced price prediction platform. Many existing platforms lack comprehensive solutions, such as precise realtime data, dynamic notifications, and sophisticated analytical tools, limiting users’ ability to make informed investment decisions and respond effectively to market changes. This research focuses on creating a complete web-based platform that uses AI models, including linear regression, LSTM, and RNN, to predict market prices based on key parameters such as the opening price, the highest price, the lowest price, and the volume of trades. The front-end is built with HTML5, CSS and JavaScript, and the back-end uses Python with Flask. Real-time data integration is achieved through APIs like ‘Yahoo Finance’ and ‘Finnhub’, which provide customization alerts and interactive analytics tools to track trends and forecast market movements.In a 7-year dataset covering AAPL, GOOGL, MSFT, AMZN and BTC, the best models achieved strong out-of-sample precision: for Apple (AAPL) R²=0.98 and RMSE=$1.42; for Amazon (AMZN) RMSE=$1.06 (lowest); for Google (GOOGL) R²=0.78 (moderate fit); and for Microsoft (MSFT) RMSE=$1.62 (good fit). The authors release a three-tier reference architecture and show that the platform delivers stable, real-time operation with precise alerting underload. Comparative analysis against recent literature indicates our results are competitive with modern deep learning baselines, while emphasizing a production-ready design.
Environmental Performance Index, Operational Effectiveness, and Cost-effectiveness of Oman’s port infrastructure and industry were assessed in this study. In relation to sustainable logistics and supply chain metrics. Data was gathered from 407 logistics and supply chain experts in Oman’s main ports and associated industries. Using a quantitative survey-based methodology. To investigate the connections between important elements such as green supply chain practices and environmental management strategies, stakeholder collaboration, regulatory compliance, For technological innovation, the study used PLS-SEM. The results show that industrial environmental performance is greatly improved by adopting Green supply chain and environmental management techniques. Stakeholder Cooperation and environmental performance were found to be significantly influenced. According to regulatory compliance. Technological innovation has shown beneficial effects. However, stakeholder collaboration had little effect on environmental performance. The results offer guidance to policymakers and business executives regarding how to prioritize sustainable practices to enhance operational and environmental results in Oman’s port and logistics industry. The study recommends investing in advanced environmental technologies, strengthening regulatory oversight, promoting sustainable practices across supply chains, and reinforcing environmental management systems to improve environmental performance in Oman’s port facilities and logistics industry.
